Category Archives: Health and Nutrition



D. Almond

Journal of Political Economy, Vol. 114, No. 4 (2005) pp. 672-712

A Short Summary 

In a Nutshell

According to fetal origins hypothesis. Many health conditions that occur in an individual’s lifetime can be traced back to the course of fetal development. This could have serious economic consequences and hence indicate policy that might be used to combat poor pre-birth conditions in order to improve economic outcomes and aggregate health. In order to evaluate these claims a unique natural experiment is analyzed: the 1918 Spanish flue pandemic in the US.

This pandemic struck in Oct 1918 and was over by beginning of 1919 implying that cohorts born just months apart experienced very different in utero conditions. Different states were affected differently. Exploiting this variation in a RDD and DID design (comparing cohorts by birthdate, and comparing cohorts by state) using census data the study finds that virtually all examined socio-economic outcomes were affected. Children of exposed mothers were 15% less likely to graduate from high school, wages were 5-9% lower for men and likelihood of being poor rose 15%.

The responsiveness of labour market outcomes to fetal health has significant implications for health economics – i.e. policies to improve fetal health may have a multiplier effect to any policies that seek to improve the educational system.



S.Subramanian & A. Deaton

Journal of Political Economy Vol. 104, No.1 (Feb., 1996) pp.133-162


Principal Research Question and Key Result How elastic is caloric intake with respect to expenditure? The key result places the elasticity at 0.55.
Theory The development literature posits two links between income and caloric intake, with causality operating in different directions for each theory. Firstly, some argue that productivity depends upon nutrition. In extreme cases this can lead to a poverty trap as those who do not get enough to eat are insufficiently productive to make it profitable for employers to hire them above the wage threshold that would give them sufficient income to purchase enough calories. This implies unemployment and underemployment and an inability of businesses to attract sufficient workers. This inability to work is thought to act as a break on economic growth.

The other line of enquiry holds that nutrition is conditioned by income, and in this regard we try to estimate the Engel curve, which is how nutrition changes with respect to income. The demand for calories should rise with income, perhaps not 1:1 as people substitute quality for quantity, but with an elasticity greater than 0.

However, some academics have argued that elasticity is 0, in particular Bouis . This implies that economic growth will not result in improvements in nutrient intakes. If this is true then there is a real challenge for economists who tend to measure welfare in terms of income. If income does not guarantee a good standard of living (perhaps because people do not really know what is good for them), then the whole approach to increasing incomes needs to be rethought. In a further example, economists believe that the ability to substitute across goods is welfare enhancing as it allows consumers to protect themselves from price shocks on one particularly product. However, a nutritionist is only concerned with individuals having a good diet, and if in fact as incomes rise people substitute toward food that is bad for them, then this decreases welfare irrespective of the newfound ability to substitute.

Motivation See above
  • National Sample Survey for rural households in Maharashtra, western India.
  • 5,630 households, 10 in each of 563 villages
  • Report expenditure on over 300 items including 149 food items (and then tables are used to convert these into calories)
  • No income data collected, so total household expenditure is used as the welfare measure.


Method Summary Stats etc.

They regress total available calories on the number of meals given to guests, employees and those taken at home to find out how many calories are contained in each type of meal. They then subtract/add those meals given away/received to the total calories available, to create an adjusted figure. If they did not do this the elasticity for richer households would be grossly overstated as they have a large number of available calories as they give away many more meals to employees/guests. When the data are tabulated it becomes clear that the poor spend a lot less money per 1000 calories than the rich. This is because coarse cereals provide a much larger share of caloric intake of the poor. As people get richer it appears they substitute between food groups away from cereals toward dairy and meat products etc. which have many fewer calories per unit of expenditure. Thus although the total food elasticity is 0.772, the price of calories elasticity of 0.32 drives a wedge between the food and calories elasticites. This is due to substitution.

Empirical Method

There are two strategies. The first models the log per capita calorie expenditure against the log of total household expenditure using smooth local regression techniques, similar to kernel density analysis, with weights assigned such that observations closer to the x in question have greater weight. The bandwidth is set such as to balance the variance-bias tradeoff. Rather than doing a regression for each value of x they use an evenly spaced 100 grid points ini the distribution of log per capita expenditure. This method is useful for modelling non-linearities between bivariate variables, but becomes too complex when controlling for other variables.

Thus there is also an OLS specification that can take into account the effect of omitted variables not included in the above method. Such important covariates include household demographics including size of household and age, as well as individual village effects.

Results The results of the kernel analysis do not reproduce the findings of elasticities close to 0. The predicted line is close to linear and negative such that lower expenditure households have a greater elasticity (0.65) that higher expenditure  households (0.4), which is to be expected. The elasticity estimates do not contain 0 even when applying 2 standard deviation confidence bands.

Similar analysis investigating price per calorie and expenditure indicate that price per calorie increased with expenditure, this is found to be the product of quality upgrading within food groups and between food groups.

In the OLS results including household size decreases the coefficient from 0.4 to around 0.35, and this is then robust to the inclusion of other variables including caste, labour type, religion, and other demographic variables. The elasticity is food as regards to income is 0.75, and this is pretty evenly split between increase in calories available (0.37) and price per calorie (0.38). Thus the elasticity of price per calorie with regards to expenditure, drives a wedge between the amount of food purchased and the amount of calories that are actually available.

Robustness Various controls added. They check for non-linearity using the specification noted above.


Interpretation The data are not easily reconcilable with a poverty nutrition trap. Food calories are cheap in the study and as households become richer they substitute toward lower calorie foods, which is not consistent with a picture of individuals unable to work due to lack of food, as if that were the case elasticities would be much greater. Additionally, food calories are found to be cheap in the region, and as such if there is poverty trap it is one that is easy to escape.


Problems The analysis is based upon available calories, and thus if wastage etc. is a salient issue, then results may be biased.

Expenditure is a good proxy for income but it is not perfect as it may involve spending money from relatives, remittances, and government programmes, and spending using those types of fund may exhibit different patterns than more traditional labour income.

Endogeneity: if hunger caused poverty as well as poverty causing hunger there would be reverse causality problems. The argument is that lack of hunger reduces productivity and thus wage earning capability which prevents calories from  being purchased. Hunger thus creates a “poverty trap”. They cannot rule this out by using e.g. IV regression, but they claim that as the 600 extra calories that are needed to sustain physical work, could be purchased for 4% of the daily wage, that the barriers to sufficient nutrition are not high enough to create a poverty trap.  




D. Acemoglu & S. Johnson

Journal of Political Economy, Vol. 115 No. 6 (2007) pp. 925-85

Principal Research Question and Key Result Have the 21st century’s increases in life expectancy translated into increased economic growth? No statistically significant effect on growth is found and in fact per capita indicators show significant negative association with life expectancy, indicating that any aggregate growth gains are more than offset by increases in population and birth rates.


Theory In neoclassical growth theory increased life expectancy raises that population which initially reduced capital to labour ratios thus depressing income per capita. This may be compensated for in the medium/long run by higher output as more people enter the labour market and more capital is accumulated. This compensation may eventually exceed the initial per capita measures is there are significant productivity benefits from longer life expectancy. This may not occur however if some factors of production are supplied inelastically (not sure I get this last bit). None of this should be taken as an indication that welfare will not be greatly increased from increased life expectancy, only that there is no discernible relationship with GDP.


Motivation There is a growing consensus that improving health outcomes can have indirect payoffs through accelerating economic growth. Whilst Marco studies of these effects are plagued by problems of comparability and also that countries characterized by ill health are also disadvantaged in other ways, micro studies (which show positive effects of health outcomes on growth) cannot account for general equilibrium effects. The most important of these would be that as there are diminishing returns to effective units of labour, micro studies that cannot account for the effect of population growth pursuant to improvements in health, will tend to overstate the economic returns from doing so. This paper takes a novel approach by instrumenting fairly successfully for life expectancy by exploiting what they term the international epidemiological transition that occurred in the 1940s which was essentially the development and diffusion of curative and preventative medicine.


Strategy There is a long differences strategy (primarily to look at effects of life expectancy on other demographic variables), and an IV strategy (to look at effects of life expectancy on wealth variables).

The long differences strategy compares variables in 1940 (prior to the transition) with 1980 (being prior to HIV). By taking differences they are excluding all the country fixed components of the growth model ( technology, initial human capital etc.), with an additional vector of controls. The dependent variables of interest are GDP, population, births, and age composition of the country. The primary explanatory variable is life expectancy.

The IV strategy uses potential mortality to instrument for life expectancy. They collect data comparable data for 15 of the most important infectious diseases, and create time dummies for when interventions in that disease started to occur at the medicinal frontier. The instrument is constructed as follows:

Mit = ∑[(1-Idt)Mdi40 + IdtMdFt

Mdit denotes mortality in country I from disease d at time t. Idt is a dummy for intervention for disease d at time t (and thus equla to 1 for all dates after the intervention). Mdi40 is the pre-intervention mortality from disease d in country I, and MdFt is the mortality form disease d at the technological frontier after the intervention.

(1-Idt)Mdi40this part of the expression will equal the mortality rate in the country before there is an intervention as I only equals 1 when there has been an intervention, and so when there is no intervention 1-I = 1 and 1*Mdi40 equals the mortality rate pre intervention. Then when there is an intervention I turns on to equal 1, and this part of the expression will equal zero.

IdtMdFt – this part of the expression will equal zero in the pre-intervention period, and the mortality rate at the frontier after the intervention turns the dummy to 1.

Critically the value of the predicted mortality derived is in no way dependent upon how successful a country is at implementing the intervention. The dummy switches to one for all countries at the same time, which strengthens the exclusion restriction, as variations in predicted mortality are in no way correlated with the country specific error. Additionally, the Mdft is set to zero such that predicted mortality from a disease equals zero after the intervention, and so is uncorrelated with the error.

They show that there is a strong first stage relationship between the predicted mortality and life expectancy, and this is the case even excluding the richer countries from the sample

Results Long difference: the 1940-80 estimate of the effect of life expectancy on population is 1.6 which suggests that a 1% increase in life expectancy causes a 1.6% increase in the population. The coefficient on births is 2.35 and the on the % of population under 20 is 0.94. These are robust to using longer time windows (1940-2000). When the GDP measures are the dependent variable, there is a 0.85% increase in GDP associated with 1% increase in life expectancy, but this is insufficient to compensate for the population effects, so GDP per capita and GDP per working age population are both significant and negative. These results are only tentative, as there are endogeneity problems i.e. richer countries may invest more in health.

The first stage yields a coefficient implies that improving predicted mortality will improve life expectancy by 21% and this is significant at the 1% level. This is the case then shorter time windows are used also. The run the 2SLS IV estimate with different dependent variables:

  • Population – countries with a larger decline in predicted mortality experienced larger increases in population. The coefficient on the baseline sample is 1.65 and is significant at the 1% level.
  • Births – coefficients vary between 2.15 and 2.9 which are quite high
  • % Population under 20 – the coefficient is large and significant, but using the longer time window becomes insignificant indicating that the new drugs saved the lives of young people initially, but this effect was averaged out over time.
  • GDP – the coefficient on life expectancy is now only 0.32 and is not significant, although given the size of the standard errors, economically significant effects are impossible to rule out. This effect is smaller when only looking at the subsample of poorer countries.
  • GDP per capita – any increase in GDP was more than offset by the population growth as the coefficient is now negative and significant, and this seems to have affected working age population as much as any.


  • They use some alternative instruments which I will not go into, results are similar.
  • In the IV estimates they control for institutions, initial GDP, continent dummies etc. and there are no significant changes to the estimates.
  • They do a falsification test by showing that lead predicted mortality had no effect on life expectancy, and they further see if predicted mortality in 1940 is a good predictor of life expectancy in the period of 1900-1940 which it is not indicating that preexisting trends are not driving the validity of the instrument.
  • The analysis only takes into account mortality. If the epidimiological transition has also greatly reduced morbidity and this has had an effect on individual productivity, then there may have been economic effects not captured by life expectancy measures.
  • If the disease environment in 1940 in some way was a reflection , or cause of the growth trajectory/path of a country, and that trajectory is persistent over time, then using the 1940-80 window may cause endogeneity issues, as outcomes then, and in 1980 will be correlated with this unobserved trajectory variable and this could be biasing results.
  • The results may not be applicable to the world today as the transition was a very singular event, and the disease environment today is very different particularly taking into account HIV which tends to affect adults in the prime of their lives, rather than the diseases analyses here whose burden fell predominantly on young people outside the workforce.
Implications Increasing life expectancy increases the population, and birth rates did not decline sufficiently for there to be any meaningful compensation in terms of increased productivity. Overall this has led to a decrease in income per capita. This would seem to suggest that for economic (as opposed to simply welfare) benefits to be felt from improved health outcomes, there needs to be concurrent efforts to improve productivity such that capital can be accumulated at a faster rate than the population growth diminished capital per worker. Naturally none of this means that promoting health outcomes does not have its own intrinsic value.





J.L. Gallup & J.D. Sachs

Journal of Tropical Medicine and Hygiene, Vol. 64 (2001) pp. 85-96

A Summary

What effect does Malaria have on GDP growth in Malaria endemic regions? They estimate that countries with severe malaria incidence have on average 1.3% lower growth rates per year.

It is quite clear that poor countries predominate in the same regions as malaria, and that economic growth in those regions is much lower than elsewhere. What is not clear is whether the observed correlation between malaria and low growth are causal or merely coincidental. Specifically, malaria could merely be proxying for other determinants of low growth such as poor quality tropical soils, inaccessibility to world markets, different patterns of colonization etc. However, in regressions that include these types of geographic variables, malaria is still found to have a significant effect. This is also the case when Africa is excluded from the sample, which shows that it is not merely Africa that is driving the results.

What is not clear however is whether malaria is a cause of poor growth, or the product of poor growth? If for example, malaria eradication/prevention becomes possible only when countries reach a certain level of development, then the true effect runs from growth to malaria, not the other way around. The authors claim that this interpretation does not tally with the facts. They claim that Malaria eradication is determined by ecology and climate, and not by personal behviour, general development, or the extent of urbanization (although they may be important, they are of second order importance). This can be shown when it is considered that the regions with the worst malaria in 1965 had the least reduction in malaria in the next three decades. They claim that in endemic areas of Africa with up to 300 infectious bites per night, control simply is not possible and so it cannot be argued that control is the effect of growth.

Whilst seductive, this line of reasoning is not really sufficient to prove the case. As they attest to there have been successful eradications of malaria. For example in Greece where up to a quarter of the population was infected in the malaria season, it was successfully eradicated. This also occurred in Italy, and other parts of Southern Europe. Whilst the virulence and prevalence of the disease in those areas may indeed have been less severe than in Africa, it is not at all clear that they were not able to eradicate the burden due to comparatively higher levels of development.  The sort of largely anecdotal evidence presented in the paper is not really sufficient to substantiate the claim that causality runs from malaria to growth and not vice-versa.

Additionally, they show growth patterns for Taiwan that successfully eradicated malaria, and compare their pre and post malaria growth rates with that of the rest of East Asia, and the simple difference in difference is only +0.9% which is hardly compelling, and is not estimated using regression techniques so no statistical significance can be evaluated. Additionally Mauritius did not see growth after eradicating malaria, although this may have been due to the closed nature of their economy – this indicates either that the effect does not run from malaria to growth, or that malaria interacts with other features of the economy such that simple eradication does not guarantee a growth episode.

They run a cross country growth regression using a new malaria index which is the fraction of population living in areas with high malaria risk in 1965 times that fraction of malaria cases in 1990 that are due to the most severe malaria vector. On the right hand side are initial income levels, human capital, institutional variables, geographic and economic. The results indicate that both initial levels of the index and subsequent changes are significantly associated with GDP growth. One might argue that the malaria index is only capturing the worst type of malaria, and there seems to be little sense in leaving out other types, as restricting to the worst variety essentially confines analysis to Africa, and Haiti. Indeed when Africa is excluded the results persist, however this could be being driven exclusively by Haiti, which is extremely poor, and burdened with very high malaria levels. Nevertheless, the results show that for a 10% reduction in the malaria index on average would result in 0.3% growth increase.

In order to combat remaining problems of endogeneity they instrument for the malaria index using the prevalence of mosquito vectors in each country in 1952. No first stage is reported and thus it is not possible to evaluate the strength of the first stage. Additionally it is not clear that the instrument and the index are substantially different, they both seem basically to be measuring the same thing. However, the results do not substantially change the OLS estimates.

When they include other tropical diseases in the regressions they do not find any significant effects. Whilst this indicates that malaria is not proxying for other diseases, it is not theoretically clear why malaria should have significant effects, but yellow fever etc. should not. This leads me to question he results.

There is little agreement as to what channels malaria works through to lead to lower GDP. Suggested in the paper are:

  • Lower productivity due to morbidity (although potentially mitigated by partial immunity)
  • Lower levels of cognitive development (see for example the Spanish flu paper in EC454)
  • Malaria keeps away tourists and investors.
  • Malaria limits internal movement of people and hence goods/services.

If the results are taken at face value, then a huge amount of emphasis should be put on prevention and eradication. This is in fact what we see in the development community. LLINs are being widely distributed and provide a very cost effective way of reducing the incidence of malaria, particularly since only 50% of a community need to sleep under a net in order for spillovers to be created (the mosquito dies when it lands on the net so is unable to bite anyone else). Naturally these interventions are not made solely with GDP in mind as there are welfare benefits from not being sick that are potentially more of concern than long run GDP growth.